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  <h1>Source code for pgmpy.estimators.BicScore</h1><div class="highlight"><pre>
<span></span><span class="ch">#!/usr/bin/env python</span>

<span class="kn">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">log</span>

<span class="kn">from</span> <span class="nn">pgmpy.estimators</span> <span class="k">import</span> <span class="n">StructureScore</span>


<div class="viewcode-block" id="BicScore"><a class="viewcode-back" href="../../../estimators.html#pgmpy.estimators.BicScore.BicScore">[docs]</a><span class="k">class</span> <span class="nc">BicScore</span><span class="p">(</span><span class="n">StructureScore</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Class for Bayesian structure scoring for BayesianModels with Dirichlet priors.</span>
<span class="sd">        The BIC/MDL score (&quot;Bayesian Information Criterion&quot;, also &quot;Minimal Descriptive Length&quot;) is a</span>
<span class="sd">        log-likelihood score with an additional penalty for network complexity, to avoid overfitting.</span>
<span class="sd">        The `score`-method measures how well a model is able to describe the given data set.</span>

<span class="sd">        Parameters</span>
<span class="sd">        ----------</span>
<span class="sd">        data: pandas DataFrame object</span>
<span class="sd">            datafame object where each column represents one variable.</span>
<span class="sd">            (If some values in the data are missing the data cells should be set to `numpy.NaN`.</span>
<span class="sd">            Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)</span>

<span class="sd">        state_names: dict (optional)</span>
<span class="sd">            A dict indicating, for each variable, the discrete set of states (or values)</span>
<span class="sd">            that the variable can take. If unspecified, the observed values in the data set</span>
<span class="sd">            are taken to be the only possible states.</span>

<span class="sd">        complete_samples_only: bool (optional, default `True`)</span>
<span class="sd">            Specifies how to deal with missing data, if present. If set to `True` all rows</span>
<span class="sd">            that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
<span class="sd">            every row where neither the variable nor its parents are `np.NaN` is used.</span>
<span class="sd">            This sets the behavior of the `state_count`-method.</span>

<span class="sd">        References</span>
<span class="sd">        ---------</span>
<span class="sd">        [1] Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
<span class="sd">        Section 18.3.4-18.3.6 (esp. page 802)</span>
<span class="sd">        [2] AM Carvalho, Scoring functions for learning Bayesian networks,</span>
<span class="sd">        http://www.lx.it.pt/~asmc/pub/talks/09-TA/ta_pres.pdf</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">BicScore</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="BicScore.local_score"><a class="viewcode-back" href="../../../estimators.html#pgmpy.estimators.BicScore.BicScore.local_score">[docs]</a>    <span class="k">def</span> <span class="nf">local_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">variable</span><span class="p">,</span> <span class="n">parents</span><span class="p">):</span>
        <span class="s2">&quot;Computes a score that measures how much a </span><span class="se">\</span>
<span class="s2">        given variable is </span><span class="se">\&quot;</span><span class="s2">influenced</span><span class="se">\&quot;</span><span class="s2"> by a given list of potential parents.&quot;</span>

        <span class="n">var_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_names</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span>
        <span class="n">var_cardinality</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">var_states</span><span class="p">)</span>
        <span class="n">state_counts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_counts</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">parents</span><span class="p">)</span>
        <span class="n">sample_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
        <span class="n">num_parents_states</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">state_counts</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>

        <span class="n">score</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">for</span> <span class="n">parents_state</span> <span class="ow">in</span> <span class="n">state_counts</span><span class="p">:</span>  <span class="c1"># iterate over df columns (only 1 if no parents)</span>
            <span class="n">conditional_sample_size</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">state_counts</span><span class="p">[</span><span class="n">parents_state</span><span class="p">])</span>

            <span class="k">for</span> <span class="n">state</span> <span class="ow">in</span> <span class="n">var_states</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">state_counts</span><span class="p">[</span><span class="n">parents_state</span><span class="p">][</span><span class="n">state</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                    <span class="n">score</span> <span class="o">+=</span> <span class="n">state_counts</span><span class="p">[</span><span class="n">parents_state</span><span class="p">][</span><span class="n">state</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">log</span><span class="p">(</span><span class="n">state_counts</span><span class="p">[</span><span class="n">parents_state</span><span class="p">][</span><span class="n">state</span><span class="p">])</span> <span class="o">-</span>
                                                                   <span class="n">log</span><span class="p">(</span><span class="n">conditional_sample_size</span><span class="p">))</span>

        <span class="n">score</span> <span class="o">-=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">log</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">num_parents_states</span> <span class="o">*</span> <span class="p">(</span><span class="n">var_cardinality</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">score</span></div></div>
</pre></div>

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